Abstract: A system for discovering internet memes from a social media stream includes a meme detector for grouping together incoming posts that contain a common set of anomalous terms, the meme detector comparing the frequency of use of the anomalous terms against normalcy models to ensure that the use of the anomalous terms is non-recurring. The system additionally includes a meme tracker for classifying each cluster of related posts as a particular meme and, in turn, associating new related posts from the media stream with the classified cluster. In this manner, the particular set of anomalous terms used to classify each meme can be augmented to include other discriminating terms. Accordingly, the parameters, or features, associated with each meme are continuously monitored and refined, as needed, to ensure that the most comprehensive and reflective cluster of posts for each meme is collected from the social media stream in an efficient fashion.

Abstract: A system for discovering internet memes from a social media stream includes a meme detector for grouping together incoming posts that contain a common set of anomalous terms, the meme detector comparing the frequency of use of the anomalous terms against normalcy models to ensure that the use of the anomalous terms is non-recurring. The system additionally includes a meme tracker for classifying each cluster of related posts as a particular meme and, in turn, associating new related posts from the media stream with the classified cluster. In this manner, the particular set of anomalous terms used to classify each meme can be augmented to include other discriminating terms. Accordingly, the parameters, or features, associated with each meme are continuously monitored and refined, as needed, to ensure that the most comprehensive and reflective cluster of posts for each meme is collected from the social media stream in an efficient fashion.